{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Lecture15 : 课程回顾和复习 </center>  \n",
    " \n",
    "## <center> Instructor: Dr. Hu Chuan-Peng </center> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlines\n",
    "\n",
    "| 序号  |                    课程内容                     |\n",
    "| :--: | :--------------------------------------------: |\n",
    "|  1   |                    课程介绍                     |  \n",
    "|  2   |                  Bayes' Rule                   |\n",
    "|  3   |        The Beta-Binomial Bayesian Model        |\n",
    "|  4   | Balance and Sequentiality in Bayesian Analyses |\n",
    "|  5   |          Approximating the Posterior           |\n",
    "|  6   |              MCMC under the Hood               |\n",
    "|  7   |        Posterior Inference & Prediction        |\n",
    "|  8   |           A Simple Normal Regression           |\n",
    "|  9   |                  Bayes factors                 |\n",
    "|  10  |              Multiple regression               |\n",
    "|  11  |         Evaluating Regression Models           |\n",
    "|  12  |            GLM: Logistic Regression            |\n",
    "|  13  |             Hierarchical Models 1              |\n",
    "|  14  |             Hierarchical Models 2              |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对本课程最简略的概括\n",
    "\n",
    "- 一个概念：贝叶斯视角下的参数\n",
    "- 一个公式：贝叶斯公式\n",
    "- 一个算法：马尔科夫链蒙特卡洛（MCMC）\n",
    "- 一个软件：PyMC\n",
    "- 一个workflow：贝叶斯分析的工作流程\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个概念\n",
    "**模型参数是随机的，是概率分布**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 一个公式\n",
    "\n",
    "$$  \n",
    "P(A|B) = \\frac{P(A) * P (B | A)}{P(B)}  \n",
    "$$  \n",
    "\n",
    "- $P(A|B)$: 后验概率\n",
    "- $P(A)$: 先验概率\n",
    "- $P(B|A)$: 似然函数\n",
    "- $P(B)$: Marginal likelihood\n",
    "\n",
    "<div style=\"padding-bottom: 50px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个算法\n",
    "马尔科夫链蒙特卡洛（MCMC）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个软件包\n",
    "**PyMC**\n",
    "\n",
    "通过PyMC学习使用概率编程语言(probability programming language)，实现贝叶斯推断。\n",
    "\n",
    "![Image Name](https://cdn.kesci.com/upload/sl1bdlkzgo.png?imageView2/0/w/640/h/640)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个数据分析流程\n",
    "\n",
    "**Bayesian Workflow**\n",
    "\n",
    "![Image Name](https://cdn.kesci.com/upload/sozk5mh1vf.png?imageView2/0/w/960/h/960)\n",
    "\n",
    "其他相关指南参考：\n",
    "\n",
    "> Kruschke, J.K. Bayesian Analysis Reporting Guidelines. Nat Hum Behav 5, 1282–1291 (2021). https://doi.org/10.1038/s41562-021-01177-7\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一个完整的例子\n",
    "\n",
    "### 研究问题\n",
    "\n",
    "“**随机点运动范式中，反应时间如何受到随机点运动方向一致性比例的影响，如果会的话，其影响程度是怎么样的**？”"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据\n",
    "\n",
    "Evans et al.（2020, Exp. 1） 的数据，包括57名被试的数据，单因素被试内实验设计，自变量为2个水平。\n",
    "\n",
    "<center>  \n",
    "    <table>  \n",
    "            <tr>  \n",
    "                <td><img src=\"https://cdn.kesci.com/upload/sjwnyi477j.gif?imageView2/0/w/400/h/400\" alt=\"\"></td>  \n",
    "                <td><img src=\"https://cdn.kesci.com/upload/sjwnyt1yq4.gif?imageView2/0/w/400/h/400\" alt=\"\"></td>  \n",
    "            </tr>  \n",
    "            <tr>  \n",
    "                <td>一致性5%</td>  \n",
    "                <td>一致性10%</td>  \n",
    "            </tr>  \n",
    "    </table>  \n",
    "</center>  \n",
    "\n",
    "> Evans, N. J., Hawkins, G. E., & Brown, S. D. (2020). The role of passing time in decision-making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(2), 316–326. https://doi.org/10.1037/xlm0000725  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
    }
   ],
   "source": [
    "# 导入 pymc 模型包，和 arviz 等分析工具 \n",
    "import pymc as pm\n",
    "import arviz as az\n",
    "import seaborn as sns\n",
    "import scipy.stats as st\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import ipywidgets\n",
    "import bambi as bmb\n",
    "\n",
    "# 忽略不必要的警告\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>blkNum</th>\n",
       "      <th>trlNum</th>\n",
       "      <th>coherentDots</th>\n",
       "      <th>numberofDots</th>\n",
       "      <th>percentCoherence</th>\n",
       "      <th>winningDirection</th>\n",
       "      <th>response</th>\n",
       "      <th>correct</th>\n",
       "      <th>eventCount</th>\n",
       "      <th>averageFrameRate</th>\n",
       "      <th>RT</th>\n",
       "      <th>Coherence</th>\n",
       "      <th>subj_id</th>\n",
       "      <th>obs_id</th>\n",
       "      <th>log_RTs</th>\n",
       "      <th>global_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>left</td>\n",
       "      <td>left</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>15.700</td>\n",
       "      <td>1465</td>\n",
       "      <td>0</td>\n",
       "      <td>66670</td>\n",
       "      <td>1</td>\n",
       "      <td>7.289611</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>left</td>\n",
       "      <td>left</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>15.375</td>\n",
       "      <td>1626</td>\n",
       "      <td>0</td>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>7.393878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>right</td>\n",
       "      <td>right</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>15.346</td>\n",
       "      <td>2411</td>\n",
       "      <td>1</td>\n",
       "      <td>66670</td>\n",
       "      <td>3</td>\n",
       "      <td>7.787797</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>left</td>\n",
       "      <td>left</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16.113</td>\n",
       "      <td>993</td>\n",
       "      <td>0</td>\n",
       "      <td>66670</td>\n",
       "      <td>4</td>\n",
       "      <td>6.900731</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>66670</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>left</td>\n",
       "      <td>left</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>15.564</td>\n",
       "      <td>1028</td>\n",
       "      <td>1</td>\n",
       "      <td>66670</td>\n",
       "      <td>5</td>\n",
       "      <td>6.935370</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      subject  blkNum  trlNum  coherentDots  numberofDots  percentCoherence  \\\n",
       "2239    66670       2       2             2            40                 5   \n",
       "2240    66670       2       3             2            40                 5   \n",
       "2241    66670       2       4             4            40                10   \n",
       "2243    66670       2       6             2            40                 5   \n",
       "2244    66670       2       7             4            40                10   \n",
       "\n",
       "     winningDirection response  correct  eventCount  averageFrameRate    RT  \\\n",
       "2239             left     left        1          23            15.700  1465   \n",
       "2240             left     left        1          25            15.375  1626   \n",
       "2241            right    right        1          37            15.346  2411   \n",
       "2243             left     left        1          16            16.113   993   \n",
       "2244             left     left        1          16            15.564  1028   \n",
       "\n",
       "      Coherence  subj_id  obs_id   log_RTs  global_id  \n",
       "2239          0    66670       1  7.289611          0  \n",
       "2240          0    66670       2  7.393878          1  \n",
       "2241          1    66670       3  7.787797          2  \n",
       "2243          0    66670       4  6.900731          3  \n",
       "2244          1    66670       5  6.935370          4  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用 pandas 导入示例数据\n",
    "try:\n",
    "  df_raw  = pd.read_csv(\"/home/mw/input/bayes3797/evans2020JExpPsycholLearn_exp1_full_data.csv\") \n",
    "except:\n",
    "  df_raw  = pd.read_csv('data/evans2020JExpPsycholLearn_exp1_full_data.csv')\n",
    "# 为每个被试建立索引 'subj_id' 和 'obs_id'\n",
    "df = df_raw.copy()\n",
    "df['subj_id'] = df_raw['subject']\n",
    "df['obs_id'] = df_raw.groupby('subject').cumcount() + 1\n",
    "\n",
    "# 对反应时间取对数\n",
    "df[\"log_RTs\"] = np.log(df[\"RT\"])\n",
    "\n",
    "# 为每一行生成全局唯一编号 'global_id'\n",
    "df['global_id'] = range(len(df))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型设定\n",
    "\n",
    "建立三个不同的模型来探讨反应时间与一致性比例之间的关系。\n",
    "\n",
    "#### 模型1：完全池化模型\n",
    "\n",
    "**目的：不考虑随机点运动方向一致性的比例对反应时间的影响**\n",
    "\n",
    "\n",
    "$$  \n",
    "\\begin{array}{lcrl}  \n",
    "Y_i | \\beta_0, \\beta_1, \\sigma & \\stackrel{ind}{\\sim} N\\left(\\mu_i, \\sigma^2\\right) \\;\\; \\text{ with } \\;\\; \\mu_i = \\beta_0 + \\beta_1X_i \\\\  \n",
    "\\end{array}  \n",
    "$$ \n",
    "\n",
    "\n",
    "#### 模型2：部分池化模型（变化截距）\n",
    "\n",
    "**目的：随机点运动方向的一致性与反应时间之间的关系在被试内有什么不同**\n",
    "\n",
    "$$  \n",
    "\\begin{array}{rll}\n",
    "&\\mu_{\\beta_0}, \\sigma_{\\beta_0} & \\text{Layer 3: 总体水平} \\\\  \n",
    "\\beta_{0j} | \\mu_{\\beta_0}, \\sigma_{\\beta_0}  & \\stackrel{\\text{ind}}{\\sim} N(\\mu_{\\beta_0}, \\sigma_{\\beta_0}^2)  & \\text{Layer 2: 组水平} \\\\  \n",
    "Y_{ij} | \\beta_{0j}, \\beta_{1}, \\sigma_y & \\sim N(\\mu_{ij}, \\sigma_y^2) \\;\\; \\text{ with } \\;\\;  \\mu_{ij} = \\beta_{0j}  + \\beta_{1} X_{ij} & \\text{Layer 1: 试次水平/数据点} \\\\  \n",
    "\\end{array}  \n",
    "$$\n",
    "\n",
    "#### 模型3：部分池化模型（变化斜率和截距）\n",
    "\n",
    "**目的：考虑截距和斜率共同变化的情况，并全局参数进行定义，即**                         \n",
    "\n",
    "$$  \n",
    "\\begin{array}{rll}  \n",
    "\n",
    "&\\mu_{\\beta_0}, \\mu_{\\beta_1}, \\sigma_{\\beta_0}, \\sigma_{\\beta_1} & \\text{Layer 3: 总体水平} \\\\  \n",
    "\\beta_{0j} | \\mu_{\\beta_0}, \\sigma_{\\beta_0}  & \\stackrel{ind}{\\sim} N(\\mu_{\\beta_0}, \\sigma_{\\beta_0}^2) & \\text{Layer 2: 组水平（截距在被试间的变化）} \\\\  \n",
    "\\beta_{1j} | \\mu_{\\beta_1}, \\sigma_{\\beta_1}  & \\stackrel{ind}{\\sim} N(\\mu_{\\beta_1}, \\sigma_{\\beta_1}^2) & \\text{Layer 2: 组水平（斜率在被试间的变化）} \\\\  \n",
    "Y_{ij} | \\beta_{0j}, \\beta_{1j}, \\sigma_y & \\sim N(\\mu_{ij}, \\sigma_y^2) \\;\\; \\text{ with } \\;\\; \\mu_{ij} = \\beta_{0j} + \\beta_{1j} X_{ij} & \\text{Layer 1: 试次水平/数据点} \\\\  \n",
    "\\end{array}  \n",
    "$$  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "## 模型1：完全池化模型\n",
    "complete_pooled_model = bmb.Model(\"log_RTs ~ 1 + Coherence\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "## 模型2：随机截距模型\n",
    "var_inter_model = bmb.Model(\"log_RTs ~ 1 + Coherence + (1|subj_id)\", df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型3：随机截距和斜率模型   \n",
    "var_both_model = bmb.Model(\"log_RTs ~ Coherence + (Coherence|subj_id)\",df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合数据及MCMC评估\n",
    "\n",
    "接下来会对3个模型进行数据拟合、MCMC评估及后验计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**模型1**：完全池化模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [log_RTs_sigma, Intercept, Coherence]\n"
     ]
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n"
     ]
    }
   ],
   "source": [
    "complete_pooled_trace = complete_pooled_model.fit(idata_kwargs={\"log_likelihood\": True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>6.951</td>\n",
       "      <td>0.021</td>\n",
       "      <td>6.914</td>\n",
       "      <td>6.992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5789.0</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence</th>\n",
       "      <td>-0.057</td>\n",
       "      <td>0.029</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5754.0</td>\n",
       "      <td>3162.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_RTs_sigma</th>\n",
       "      <td>0.709</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5492.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept      6.951  0.021   6.914    6.992        0.0      0.0    5789.0   \n",
       "Coherence     -0.057  0.029  -0.115   -0.006        0.0      0.0    5754.0   \n",
       "log_RTs_sigma  0.709  0.010   0.690    0.729        0.0      0.0    5492.0   \n",
       "\n",
       "               ess_tail  r_hat  \n",
       "Intercept        2878.0    1.0  \n",
       "Coherence        3162.0    1.0  \n",
       "log_RTs_sigma    3050.0    1.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_pooled_para = az.summary(complete_pooled_trace)\n",
    "complete_pooled_para"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x410 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(\n",
    "    complete_pooled_trace,\n",
    "    var_names=[\"Coherence\"],\n",
    "    filter_vars=\"like\",\n",
    "    combined = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型2：部分池化模型（变化截距）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [log_RTs_sigma, Intercept, Coherence, 1|subj_id_sigma, 1|subj_id_offset]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f25731d7fee487183a2cf9f9bbf3604",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 44 seconds.\n",
      "There were 196 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "var_inter_trace = var_inter_model.fit(idata_kwargs={\"log_likelihood\": True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>7.065</td>\n",
       "      <td>0.309</td>\n",
       "      <td>6.500</td>\n",
       "      <td>7.688</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.016</td>\n",
       "      <td>189.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence</th>\n",
       "      <td>-0.063</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_RTs_sigma</th>\n",
       "      <td>0.559</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.544</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id_sigma</th>\n",
       "      <td>0.700</td>\n",
       "      <td>0.320</td>\n",
       "      <td>0.251</td>\n",
       "      <td>1.406</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.047</td>\n",
       "      <td>49.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[66670]</th>\n",
       "      <td>-0.139</td>\n",
       "      <td>0.310</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.016</td>\n",
       "      <td>193.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[80941]</th>\n",
       "      <td>0.137</td>\n",
       "      <td>0.310</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>0.719</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.018</td>\n",
       "      <td>188.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[81844]</th>\n",
       "      <td>-0.641</td>\n",
       "      <td>0.309</td>\n",
       "      <td>-1.269</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.016</td>\n",
       "      <td>189.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[83824]</th>\n",
       "      <td>0.085</td>\n",
       "      <td>0.310</td>\n",
       "      <td>-0.502</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.017</td>\n",
       "      <td>186.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[83956]</th>\n",
       "      <td>0.689</td>\n",
       "      <td>0.311</td>\n",
       "      <td>0.098</td>\n",
       "      <td>1.282</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.018</td>\n",
       "      <td>189.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept         7.065  0.309   6.500    7.688      0.023    0.016     189.0   \n",
       "Coherence        -0.063  0.023  -0.108   -0.021      0.000    0.000    2430.0   \n",
       "log_RTs_sigma     0.559  0.008   0.544    0.574      0.000    0.000    1448.0   \n",
       "1|subj_id_sigma   0.700  0.320   0.251    1.406      0.059    0.047      49.0   \n",
       "1|subj_id[66670] -0.139  0.310  -0.735    0.460      0.023    0.016     193.0   \n",
       "1|subj_id[80941]  0.137  0.310  -0.477    0.719      0.023    0.018     188.0   \n",
       "1|subj_id[81844] -0.641  0.309  -1.269   -0.076      0.023    0.016     189.0   \n",
       "1|subj_id[83824]  0.085  0.310  -0.502    0.673      0.023    0.017     186.0   \n",
       "1|subj_id[83956]  0.689  0.311   0.098    1.282      0.023    0.018     189.0   \n",
       "\n",
       "                  ess_tail  r_hat  \n",
       "Intercept            272.0   1.03  \n",
       "Coherence           2058.0   1.01  \n",
       "log_RTs_sigma       2011.0   1.00  \n",
       "1|subj_id_sigma       24.0   1.06  \n",
       "1|subj_id[66670]     309.0   1.03  \n",
       "1|subj_id[80941]     301.0   1.03  \n",
       "1|subj_id[81844]     277.0   1.03  \n",
       "1|subj_id[83824]     369.0   1.03  \n",
       "1|subj_id[83956]     312.0   1.03  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_inter_para = az.summary(var_inter_trace)\n",
    "var_inter_para "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_forest(var_inter_trace,\n",
    "           var_names=[\"~Intercept\", \"~sigma\"],\n",
    "           filter_vars=\"like\",\n",
    "           combined = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**模型3**：部分池化模型（变化截距和斜率）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [log_RTs_sigma, Intercept, Coherence, 1|subj_id_sigma, 1|subj_id_offset, Coherence|subj_id_sigma, Coherence|subj_id_offset]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a625715452854b0097955e21cac11498",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 76 seconds.\n",
      "There were 308 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "var_both_trace = var_both_model.fit(idata_kwargs={\"log_likelihood\": True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>7.080</td>\n",
       "      <td>0.332</td>\n",
       "      <td>6.512</td>\n",
       "      <td>7.798</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>716.0</td>\n",
       "      <td>849.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence</th>\n",
       "      <td>-0.097</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-0.308</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>966.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>log_RTs_sigma</th>\n",
       "      <td>0.556</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id_sigma</th>\n",
       "      <td>0.785</td>\n",
       "      <td>0.377</td>\n",
       "      <td>0.313</td>\n",
       "      <td>1.555</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.024</td>\n",
       "      <td>323.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id_sigma</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.449</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>666.0</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[66670]</th>\n",
       "      <td>-0.201</td>\n",
       "      <td>0.334</td>\n",
       "      <td>-0.888</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>713.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[80941]</th>\n",
       "      <td>0.168</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.489</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>714.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[81844]</th>\n",
       "      <td>-0.692</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-1.404</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>718.0</td>\n",
       "      <td>899.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[83824]</th>\n",
       "      <td>0.070</td>\n",
       "      <td>0.333</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>703.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|subj_id[83956]</th>\n",
       "      <td>0.786</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.114</td>\n",
       "      <td>1.388</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.009</td>\n",
       "      <td>731.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id[66670]</th>\n",
       "      <td>0.122</td>\n",
       "      <td>0.130</td>\n",
       "      <td>-0.114</td>\n",
       "      <td>0.327</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id[80941]</th>\n",
       "      <td>-0.061</td>\n",
       "      <td>0.131</td>\n",
       "      <td>-0.289</td>\n",
       "      <td>0.161</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id[81844]</th>\n",
       "      <td>0.107</td>\n",
       "      <td>0.127</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.003</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id[83824]</th>\n",
       "      <td>0.034</td>\n",
       "      <td>0.131</td>\n",
       "      <td>-0.191</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>1243.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coherence|subj_id[83956]</th>\n",
       "      <td>-0.182</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.405</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "Intercept                 7.080  0.332   6.512    7.798      0.012    0.009   \n",
       "Coherence                -0.097  0.124  -0.308    0.119      0.005    0.003   \n",
       "log_RTs_sigma             0.556  0.008   0.541    0.571      0.000    0.000   \n",
       "1|subj_id_sigma           0.785  0.377   0.313    1.555      0.028    0.024   \n",
       "Coherence|subj_id_sigma   0.215  0.151   0.048    0.449      0.006    0.005   \n",
       "1|subj_id[66670]         -0.201  0.334  -0.888    0.395      0.013    0.009   \n",
       "1|subj_id[80941]          0.168  0.333  -0.489    0.785      0.013    0.009   \n",
       "1|subj_id[81844]         -0.692  0.333  -1.404   -0.115      0.013    0.009   \n",
       "1|subj_id[83824]          0.070  0.333  -0.610    0.670      0.013    0.009   \n",
       "1|subj_id[83956]          0.786  0.334   0.114    1.388      0.012    0.009   \n",
       "Coherence|subj_id[66670]  0.122  0.130  -0.114    0.327      0.005    0.003   \n",
       "Coherence|subj_id[80941] -0.061  0.131  -0.289    0.161      0.005    0.004   \n",
       "Coherence|subj_id[81844]  0.107  0.127  -0.126    0.316      0.005    0.003   \n",
       "Coherence|subj_id[83824]  0.034  0.131  -0.191    0.257      0.005    0.004   \n",
       "Coherence|subj_id[83956] -0.182  0.132  -0.405    0.043      0.005    0.004   \n",
       "\n",
       "                          ess_bulk  ess_tail  r_hat  \n",
       "Intercept                    716.0     849.0   1.00  \n",
       "Coherence                    966.0    1070.0   1.01  \n",
       "log_RTs_sigma               1333.0    1339.0   1.00  \n",
       "1|subj_id_sigma              323.0     146.0   1.01  \n",
       "Coherence|subj_id_sigma      666.0     980.0   1.01  \n",
       "1|subj_id[66670]             713.0     855.0   1.00  \n",
       "1|subj_id[80941]             714.0     928.0   1.00  \n",
       "1|subj_id[81844]             718.0     899.0   1.00  \n",
       "1|subj_id[83824]             703.0     715.0   1.00  \n",
       "1|subj_id[83956]             731.0     998.0   1.00  \n",
       "Coherence|subj_id[66670]    1068.0    1224.0   1.00  \n",
       "Coherence|subj_id[80941]    1126.0    1060.0   1.01  \n",
       "Coherence|subj_id[81844]     998.0    1155.0   1.00  \n",
       "Coherence|subj_id[83824]    1125.0    1243.0   1.00  \n",
       "Coherence|subj_id[83956]    1070.0    1167.0   1.00  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_both_para = az.summary(var_both_trace)\n",
    "var_both_para"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置绘图坐标\n",
    "figs, (ax1, ax2) = plt.subplots(1,2, figsize = (20,5))\n",
    "# 绘制变化的截距\n",
    "az.plot_forest(var_both_trace,\n",
    "           var_names=[\"Coherence\\\\|subj_id\", \"~sigma\", \"~1|\", \"~Intercept\"],\n",
    "           filter_vars=\"like\",\n",
    "           combined = True,\n",
    "           ax=ax1)\n",
    "# 绘制变化的斜率\n",
    "az.plot_forest(var_both_trace,\n",
    "           var_names=[\"1|subj_id\", \"~sigma\", \"~Coherence\", \"~Intercept\"],\n",
    "           filter_vars=\"like\",\n",
    "           combined = True,\n",
    "           ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型比较\n",
    "\n",
    "##### 模型评估指标\n",
    "\n",
    "在分析模型的预测能力时，有绝对指标和相对指标，绝对指标用于衡量模型预测值与真实值之间的差异，相对指标用于比较不同模型的预测能力，通常用于不同方法或模型之间的性能对比。\n",
    "\n",
    "##### 绝对指标：\n",
    "\n",
    "* 在之前的课程中介绍过对后验预测结果进行评估的两种方法  \n",
    "\n",
    "* 一是**MAE**，即后验预测值与真实值之间预测误差的中位数，二是**within_95**，即真实值是否落在95%后验预测区间内  \n",
    "\n",
    "* 在这里调用之前写过的计算两种指标的方法，评估两种分层模型的后验预测结果\n",
    "\n",
    "\n",
    "##### 相对指标\n",
    "\n",
    "在实际操作中，我们通过 `ArViz` 的函数`az.loo`计算 $ELPD_{LOO-CV}$。  \n",
    "\n",
    "PSIS-LOO-CV 有两大优势：  \n",
    "1. 计算速度快，且结果稳健  \n",
    "2. 提供了丰富的模型诊断指标  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_pooled_model.predict(complete_pooled_trace, kind=\"pps\")\n",
    "var_inter_model.predict(var_inter_trace, kind=\"pps\")\n",
    "var_both_model.predict(var_both_trace, kind=\"pps\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (chain: 4, draw: 1000, log_RTs_obs: 2326)\n",
       "Coordinates:\n",
       "  * chain          (chain) int32 0 1 2 3\n",
       "  * draw           (draw) int32 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_RTs_obs    (log_RTs_obs) int32 0 1 2 3 4 5 ... 2321 2322 2323 2324 2325\n",
       "Data variables:\n",
       "    Intercept      (chain, draw) float64 6.948 6.952 6.953 ... 6.977 6.911 6.941\n",
       "    Coherence      (chain, draw) float64 -0.05112 -0.09796 ... -0.03254 -0.05348\n",
       "    log_RTs_sigma  (chain, draw) float64 0.6968 0.7148 0.7104 ... 0.6805 0.6917\n",
       "    log_RTs_mean   (chain, draw, log_RTs_obs) float64 6.948 6.948 ... 6.887\n",
       "Attributes:\n",
       "    created_at:                  2024-12-26T03:18:17.443034\n",
       "    arviz_version:               0.17.1\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.16.2\n",
       "    sampling_time:               19.907405138015747\n",
       "    tuning_steps:                1000\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.13.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-14a70fab-f4a6-4808-ba1f-8517dacfdcc9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14a70fab-f4a6-4808-ba1f-8517dacfdcc9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_RTs_obs</span>: 2326</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a49da9e5-3027-4816-879a-8d6b79d06497' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a49da9e5-3027-4816-879a-8d6b79d06497' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f1a53d29-556e-4fa2-b945-7ad00cd8218a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1a53d29-556e-4fa2-b945-7ad00cd8218a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b3f1c20-6191-4891-b2c0-03e658dceece' class='xr-var-data-in' type='checkbox'><label for='data-7b3f1c20-6191-4891-b2c0-03e658dceece' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-fb12e4cf-042f-4915-990b-83d149cdd091' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb12e4cf-042f-4915-990b-83d149cdd091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecea8c44-d412-4417-864a-0d3250dfa2b7' class='xr-var-data-in' type='checkbox'><label for='data-ecea8c44-d412-4417-864a-0d3250dfa2b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_RTs_obs</span></div><div class='xr-var-dims'>(log_RTs_obs)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2322 2323 2324 2325</div><input id='attrs-575ce93f-c12d-4f2a-85f0-d980a56e68ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-575ce93f-c12d-4f2a-85f0-d980a56e68ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1e83e67-b20d-4637-8997-5320c67e9830' class='xr-var-data-in' type='checkbox'><label for='data-d1e83e67-b20d-4637-8997-5320c67e9830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2323, 2324, 2325])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e89c60e-b33d-4b9d-8872-87428020598d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e89c60e-b33d-4b9d-8872-87428020598d' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.948 6.952 6.953 ... 6.911 6.941</div><input id='attrs-4c3f9ac2-e751-4d8c-9b8d-7de826fd8ee5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c3f9ac2-e751-4d8c-9b8d-7de826fd8ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c1f35d5-087b-4201-9d13-d57d92c35a29' class='xr-var-data-in' type='checkbox'><label for='data-0c1f35d5-087b-4201-9d13-d57d92c35a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[6.9484276 , 6.95188572, 6.95343913, ..., 6.94722504, 6.94267979,\n",
       "        6.91071087],\n",
       "       [6.90153807, 6.96931478, 6.95290381, ..., 6.94389367, 6.96791885,\n",
       "        6.93826767],\n",
       "       [6.97496461, 6.92278793, 6.93714468, ..., 6.92641297, 6.95582158,\n",
       "        6.95202576],\n",
       "       [6.92573615, 6.92501177, 6.96548889, ..., 6.976979  , 6.91140879,\n",
       "        6.94093996]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Coherence</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.05112 -0.09796 ... -0.05348</div><input id='attrs-7aa41acc-0a56-494d-aafd-fa980a06e380' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7aa41acc-0a56-494d-aafd-fa980a06e380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17cd9544-0818-4533-a29b-51a48a827438' class='xr-var-data-in' type='checkbox'><label for='data-17cd9544-0818-4533-a29b-51a48a827438' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.05111672, -0.09795505, -0.07760438, ..., -0.03975044,\n",
       "        -0.05171688, -0.03235639],\n",
       "       [-0.01013122, -0.08160674, -0.05821261, ..., -0.03137509,\n",
       "        -0.08918064, -0.03865577],\n",
       "       [-0.05549848, -0.05122717, -0.04489788, ..., -0.02767385,\n",
       "        -0.0625612 , -0.08709035],\n",
       "       [ 0.00391658, -0.01324593, -0.06985766, ..., -0.06286238,\n",
       "        -0.03254497, -0.05348337]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_RTs_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6968 0.7148 ... 0.6805 0.6917</div><input id='attrs-6f47a6a7-3be6-47af-9ed3-2f4f9a5e8fce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f47a6a7-3be6-47af-9ed3-2f4f9a5e8fce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe05031-0e30-41d8-8736-6dcea11d17ab' class='xr-var-data-in' type='checkbox'><label for='data-ffe05031-0e30-41d8-8736-6dcea11d17ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.69675531, 0.71481213, 0.71042364, ..., 0.69876092, 0.72270353,\n",
       "        0.70063465],\n",
       "       [0.73577512, 0.68990863, 0.7089027 , ..., 0.70789069, 0.70731282,\n",
       "        0.70646804],\n",
       "       [0.70185963, 0.72178601, 0.69031426, ..., 0.69396973, 0.71404862,\n",
       "        0.69909221],\n",
       "       [0.71420682, 0.71524757, 0.70249087, ..., 0.71218165, 0.68051867,\n",
       "        0.69166176]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>log_RTs_mean</span></div><div class='xr-var-dims'>(chain, draw, log_RTs_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.948 6.948 6.897 ... 6.887 6.887</div><input id='attrs-053faed3-f2fe-4950-a207-4b90fe324a55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-053faed3-f2fe-4950-a207-4b90fe324a55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97278d8b-42d3-43e6-9755-21838fa3c919' class='xr-var-data-in' type='checkbox'><label for='data-97278d8b-42d3-43e6-9755-21838fa3c919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[6.9484276 , 6.9484276 , 6.89731088, ..., 6.9484276 ,\n",
       "         6.89731088, 6.89731088],\n",
       "        [6.95188572, 6.95188572, 6.85393067, ..., 6.95188572,\n",
       "         6.85393067, 6.85393067],\n",
       "        [6.95343913, 6.95343913, 6.87583475, ..., 6.95343913,\n",
       "         6.87583475, 6.87583475],\n",
       "        ...,\n",
       "        [6.94722504, 6.94722504, 6.90747461, ..., 6.94722504,\n",
       "         6.90747461, 6.90747461],\n",
       "        [6.94267979, 6.94267979, 6.89096291, ..., 6.94267979,\n",
       "         6.89096291, 6.89096291],\n",
       "        [6.91071087, 6.91071087, 6.87835447, ..., 6.91071087,\n",
       "         6.87835447, 6.87835447]],\n",
       "\n",
       "       [[6.90153807, 6.90153807, 6.89140685, ..., 6.90153807,\n",
       "         6.89140685, 6.89140685],\n",
       "        [6.96931478, 6.96931478, 6.88770804, ..., 6.96931478,\n",
       "         6.88770804, 6.88770804],\n",
       "        [6.95290381, 6.95290381, 6.89469119, ..., 6.95290381,\n",
       "         6.89469119, 6.89469119],\n",
       "...\n",
       "        [6.92641297, 6.92641297, 6.89873912, ..., 6.92641297,\n",
       "         6.89873912, 6.89873912],\n",
       "        [6.95582158, 6.95582158, 6.89326039, ..., 6.95582158,\n",
       "         6.89326039, 6.89326039],\n",
       "        [6.95202576, 6.95202576, 6.8649354 , ..., 6.95202576,\n",
       "         6.8649354 , 6.8649354 ]],\n",
       "\n",
       "       [[6.92573615, 6.92573615, 6.92965273, ..., 6.92573615,\n",
       "         6.92965273, 6.92965273],\n",
       "        [6.92501177, 6.92501177, 6.91176584, ..., 6.92501177,\n",
       "         6.91176584, 6.91176584],\n",
       "        [6.96548889, 6.96548889, 6.89563123, ..., 6.96548889,\n",
       "         6.89563123, 6.89563123],\n",
       "        ...,\n",
       "        [6.976979  , 6.976979  , 6.91411662, ..., 6.976979  ,\n",
       "         6.91411662, 6.91411662],\n",
       "        [6.91140879, 6.91140879, 6.87886382, ..., 6.91140879,\n",
       "         6.87886382, 6.87886382],\n",
       "        [6.94093996, 6.94093996, 6.88745658, ..., 6.94093996,\n",
       "         6.88745658, 6.88745658]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fe2dea4-cf80-41d6-a54c-e832e9acc6ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-4fe2dea4-cf80-41d6-a54c-e832e9acc6ed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e750c55-1ecc-4940-8c09-bb3a4857b98c' class='xr-index-data-in' type='checkbox'/><label for='index-6e750c55-1ecc-4940-8c09-bb3a4857b98c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f12637c1-72d5-4c5b-b892-45e24468b220' class='xr-index-data-in' type='checkbox'/><label for='index-f12637c1-72d5-4c5b-b892-45e24468b220' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_RTs_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f47e0720-f1f6-4888-8814-2e4a6138502e' class='xr-index-data-in' type='checkbox'/><label for='index-f47e0720-f1f6-4888-8814-2e4a6138502e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;log_RTs_obs&#x27;, length=2326))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a630166e-5f10-45a2-91cd-306fef6a004b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a630166e-5f10-45a2-91cd-306fef6a004b' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-26T03:18:17.443034</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>19.907405138015747</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior_predictive730bee61-dd0f-4e56-8f4b-bf1137c71761\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior_predictive730bee61-dd0f-4e56-8f4b-bf1137c71761\" class = \"xr-section-summary\">posterior_predictive</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
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       "\n",
       ".xr-var-dims {\n",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "}\n",
       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (chain: 4, draw: 1000, log_RTs_obs: 2326)\n",
       "Coordinates:\n",
       "  * chain        (chain) int32 0 1 2 3\n",
       "  * draw         (draw) int32 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_RTs_obs  (log_RTs_obs) int32 0 1 2 3 4 5 ... 2321 2322 2323 2324 2325\n",
       "Data variables:\n",
       "    log_RTs      (chain, draw, log_RTs_obs) float64 6.407 6.604 ... 7.459 8.231\n",
       "Attributes:\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.13.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8f4adc5e-dacb-4f58-afff-60a9c3dedb7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f4adc5e-dacb-4f58-afff-60a9c3dedb7a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_RTs_obs</span>: 2326</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b66d68a2-44bf-4735-835a-5c0a6c5c19fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b66d68a2-44bf-4735-835a-5c0a6c5c19fd' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-49a5c4c9-7a3b-4085-9453-2dfda7120104' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49a5c4c9-7a3b-4085-9453-2dfda7120104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb07aa1a-aa6c-4504-ac4e-6dc2e66edaf4' class='xr-var-data-in' type='checkbox'><label for='data-bb07aa1a-aa6c-4504-ac4e-6dc2e66edaf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-92a919e3-d48d-43ec-9638-6e8dc4e987be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92a919e3-d48d-43ec-9638-6e8dc4e987be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7fa42c7-eaec-4afe-b369-2189a68f87ec' class='xr-var-data-in' type='checkbox'><label for='data-d7fa42c7-eaec-4afe-b369-2189a68f87ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_RTs_obs</span></div><div class='xr-var-dims'>(log_RTs_obs)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2322 2323 2324 2325</div><input id='attrs-68eed0b5-0a60-4f03-8e44-508dcd3aecd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68eed0b5-0a60-4f03-8e44-508dcd3aecd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a13bf08d-d72c-405d-a4fb-312f143f5a48' class='xr-var-data-in' type='checkbox'><label for='data-a13bf08d-d72c-405d-a4fb-312f143f5a48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2323, 2324, 2325])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c959dab0-9fe3-4b8f-8b22-8dfff66a176d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c959dab0-9fe3-4b8f-8b22-8dfff66a176d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_RTs</span></div><div class='xr-var-dims'>(chain, draw, log_RTs_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.407 6.604 7.527 ... 7.459 8.231</div><input id='attrs-99a20335-26fe-4637-a81d-1780f215d508' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99a20335-26fe-4637-a81d-1780f215d508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8641a29-67a8-4b80-9f89-27ff04c81164' class='xr-var-data-in' type='checkbox'><label for='data-d8641a29-67a8-4b80-9f89-27ff04c81164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[6.40671707, 6.60403998, 7.52717513, ..., 8.01473071,\n",
       "         7.07582742, 6.39597858],\n",
       "        [5.74444931, 5.7569578 , 6.75101351, ..., 6.84971169,\n",
       "         6.94266817, 6.96420616],\n",
       "        [8.08185811, 7.49391642, 7.44667862, ..., 8.26793404,\n",
       "         6.54346397, 6.3736547 ],\n",
       "        ...,\n",
       "        [8.14195767, 6.90599057, 7.75972349, ..., 8.04976369,\n",
       "         6.59641781, 6.69577564],\n",
       "        [7.60192309, 7.31046672, 8.06405693, ..., 6.30284032,\n",
       "         7.35007507, 7.26534311],\n",
       "        [7.11338563, 7.32205443, 7.80271534, ..., 7.73939876,\n",
       "         7.98886444, 7.31542021]],\n",
       "\n",
       "       [[6.12662118, 8.44813072, 8.25583462, ..., 6.89375971,\n",
       "         7.70166365, 7.54871465],\n",
       "        [7.58697456, 7.92334254, 8.18832053, ..., 6.58556212,\n",
       "         7.2482661 , 6.19090955],\n",
       "        [7.12206883, 7.2665695 , 6.67413821, ..., 6.27310741,\n",
       "         7.60839006, 6.19028248],\n",
       "...\n",
       "        [6.54713461, 6.98861251, 6.87577484, ..., 7.58981768,\n",
       "         7.62280529, 6.40457039],\n",
       "        [7.55130673, 7.08416107, 6.64977478, ..., 7.68163271,\n",
       "         7.07428463, 7.41687838],\n",
       "        [7.55403268, 6.42672009, 6.8406751 , ..., 7.65521783,\n",
       "         6.12358267, 7.42120099]],\n",
       "\n",
       "       [[6.09234345, 7.43938962, 7.3577248 , ..., 6.60983777,\n",
       "         7.03554514, 6.36232206],\n",
       "        [6.18323447, 7.50385836, 7.17377002, ..., 7.61465772,\n",
       "         7.69742439, 6.29437411],\n",
       "        [7.56860225, 7.44775056, 6.725796  , ..., 8.11699381,\n",
       "         8.39554627, 7.09789862],\n",
       "        ...,\n",
       "        [6.42457422, 7.43637263, 6.11855575, ..., 7.3118359 ,\n",
       "         6.75557617, 6.69608785],\n",
       "        [6.52042013, 5.32153506, 6.23345896, ..., 6.82006764,\n",
       "         7.44130604, 7.15390474],\n",
       "        [6.59629443, 7.40326858, 6.1613497 , ..., 6.84454596,\n",
       "         7.45893822, 8.23087794]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e46bc8ec-5f5e-4227-9dba-a9386d7b78e8' class='xr-section-summary-in' type='checkbox'  ><label for='section-e46bc8ec-5f5e-4227-9dba-a9386d7b78e8' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61fdf8d4-b4a4-4c34-a5cb-2ca7aa2d8ed4' class='xr-index-data-in' type='checkbox'/><label for='index-61fdf8d4-b4a4-4c34-a5cb-2ca7aa2d8ed4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ac76bfe4-4013-419c-82bc-1fc55e0d0346' class='xr-index-data-in' type='checkbox'/><label for='index-ac76bfe4-4013-419c-82bc-1fc55e0d0346' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_RTs_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3783c227-328a-4a9c-90ac-6278b2fd6f95' class='xr-index-data-in' type='checkbox'/><label for='index-3783c227-328a-4a9c-90ac-6278b2fd6f95' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;log_RTs_obs&#x27;, length=2326))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4017796-6ac4-4412-9d1e-4f70c9c5b523' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4017796-6ac4-4412-9d1e-4f70c9c5b523' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihoode2c8a784-ae13-473c-bc2d-8ae275a6c7c4\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihoode2c8a784-ae13-473c-bc2d-8ae275a6c7c4\" class = \"xr-section-summary\">log_likelihood</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-disabled-color: #515151;\n",
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       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
       ".xr-section-item input {\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
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       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (chain: 4, draw: 1000, log_RTs_obs: 2326)\n",
       "Coordinates:\n",
       "  * chain        (chain) int32 0 1 2 3\n",
       "  * draw         (draw) int32 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
       "  * log_RTs_obs  (log_RTs_obs) int32 0 1 2 3 4 5 ... 2321 2322 2323 2324 2325\n",
       "Data variables:\n",
       "    log_RTs      (chain, draw, log_RTs_obs) float64 -0.6775 -0.762 ... -0.9516\n",
       "Attributes:\n",
       "    created_at:                  2024-12-26T03:18:17.693780\n",
       "    arviz_version:               0.17.1\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.16.2\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.13.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8a15b902-8bdc-4a52-98de-83f058224047' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a15b902-8bdc-4a52-98de-83f058224047' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>log_RTs_obs</span>: 2326</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-10f2dc82-ff32-49ef-a311-f6b946d5cdaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-10f2dc82-ff32-49ef-a311-f6b946d5cdaf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-cd3cf9fe-4f7b-45e7-a139-d801c3d66d4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd3cf9fe-4f7b-45e7-a139-d801c3d66d4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83ca3eec-43b2-4fb2-8045-3b6d32d78e16' class='xr-var-data-in' type='checkbox'><label for='data-83ca3eec-43b2-4fb2-8045-3b6d32d78e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-3e0823bb-b823-473b-a6db-5495b42a56bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e0823bb-b823-473b-a6db-5495b42a56bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da76ef02-6cf7-4d6b-876f-062479d1818d' class='xr-var-data-in' type='checkbox'><label for='data-da76ef02-6cf7-4d6b-876f-062479d1818d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_RTs_obs</span></div><div class='xr-var-dims'>(log_RTs_obs)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2322 2323 2324 2325</div><input id='attrs-1602e3a3-e4a2-414d-b5e9-a0b87a621c8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1602e3a3-e4a2-414d-b5e9-a0b87a621c8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1b9bc2e-47f9-4168-b297-ff907f745b01' class='xr-var-data-in' type='checkbox'><label for='data-a1b9bc2e-47f9-4168-b297-ff907f745b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2323, 2324, 2325])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45e35776-6bf9-4f6a-bd1a-ae798c9ec3ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45e35776-6bf9-4f6a-bd1a-ae798c9ec3ca' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_RTs</span></div><div class='xr-var-dims'>(chain, draw, log_RTs_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6775 -0.762 ... -1.341 -0.9516</div><input id='attrs-8814941e-db2f-4690-9933-fd2db17871fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8814941e-db2f-4690-9933-fd2db17871fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-feb5fa27-80c5-474b-b082-3cdfe3816c3b' class='xr-var-data-in' type='checkbox'><label for='data-feb5fa27-80c5-474b-b082-3cdfe3816c3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.67750783, -0.76198357, -1.37431954, ..., -1.92237561,\n",
       "         -1.31889673, -0.94064268],\n",
       "        [-0.69481532, -0.77437141, -1.43660863, ..., -1.87210279,\n",
       "         -1.38134021, -1.00073752],\n",
       "        [-0.68900312, -0.76922421, -1.40097185, ..., -1.87838749,\n",
       "         -1.34635286, -0.97187957],\n",
       "        ...,\n",
       "        [-0.68053674, -0.76478505, -1.35408187, ..., -1.92026346,\n",
       "         -1.29961676, -0.92873306],\n",
       "        [-0.70940428, -0.78907027, -1.36415225, ..., -1.87539772,\n",
       "         -1.31226416, -0.95764707],\n",
       "        [-0.70939939, -0.80095351, -1.40560634, ..., -2.00275079,\n",
       "         -1.34960828, -0.96588069]],\n",
       "\n",
       "       [[-0.75120083, -0.83598527, -1.35422728, ..., -1.9376855 ,\n",
       "         -1.30419168, -0.96226768],\n",
       "        [-0.65551026, -0.73709542, -1.39879737, ..., -1.8896661 ,\n",
       "         -1.34164876, -0.95080787],\n",
       "        [-0.68769925, -0.76837601, -1.36850298, ..., -1.88305548,\n",
       "         -1.31480302, -0.94809829],\n",
       "...\n",
       "        [-0.69056562, -0.78048718, -1.37424446, ..., -1.98247137,\n",
       "         -1.31846701, -0.9379114 ],\n",
       "        [-0.69139359, -0.77031502, -1.36684678, ..., -1.86494777,\n",
       "         -1.31383175, -0.95169212],\n",
       "        [-0.67755737, -0.76070164, -1.43227874, ..., -1.90815346,\n",
       "         -1.37518912, -0.9829046 ]],\n",
       "\n",
       "       [[-0.71214098, -0.79717721, -1.30419844, ..., -1.93294619,\n",
       "         -1.25340044, -0.90925099],\n",
       "        [-0.71373545, -0.79867203, -1.33387234, ..., -1.93213734,\n",
       "         -1.28214706, -0.93026055],\n",
       "        [-0.67225561, -0.75175276, -1.37226972, ..., -1.86887692,\n",
       "         -1.31764358, -0.94469031],\n",
       "        ...,\n",
       "        [-0.67586676, -0.7508531 , -1.33199372, ..., -1.82179598,\n",
       "         -1.27996463, -0.92620015],\n",
       "        [-0.68847038, -0.78536001, -1.42601563, ..., -2.05819362,\n",
       "         -1.36669188, -0.96021805],\n",
       "        [-0.67734153, -0.76469801, -1.39749962, ..., -1.95328867,\n",
       "         -1.34062419, -0.95163074]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8d68e73-a6ff-44e7-8369-5d07eac3d014' class='xr-section-summary-in' type='checkbox'  ><label for='section-c8d68e73-a6ff-44e7-8369-5d07eac3d014' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b0dafe5f-df79-408d-8214-b6b9f17247f7' class='xr-index-data-in' type='checkbox'/><label for='index-b0dafe5f-df79-408d-8214-b6b9f17247f7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b55fe12-4c66-4e6c-add0-e61ff18ef870' class='xr-index-data-in' type='checkbox'/><label for='index-0b55fe12-4c66-4e6c-add0-e61ff18ef870' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_RTs_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-994f92c9-4b5a-48b9-9af5-cb1bf5feb12f' class='xr-index-data-in' type='checkbox'/><label for='index-994f92c9-4b5a-48b9-9af5-cb1bf5feb12f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;log_RTs_obs&#x27;, length=2326))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb548da9-5973-4bd4-854c-8546518aeb88' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb548da9-5973-4bd4-854c-8546518aeb88' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-26T03:18:17.693780</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsbc4624f4-1443-48ba-9ebc-f9ad085d01f8\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsbc4624f4-1443-48ba-9ebc-f9ad085d01f8\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 4, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int32 0 1 2 3\n",
       "  * draw                   (draw) int32 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
       "Data variables: (12/17)\n",
       "    perf_counter_start     (chain, draw) float64 9.687e+04 ... 9.688e+04\n",
       "    step_size              (chain, draw) float64 0.8867 0.8867 ... 0.9589 0.9589\n",
       "    diverging              (chain, draw) bool False False False ... False False\n",
       "    lp                     (chain, draw) float64 -2.505e+03 ... -2.505e+03\n",
       "    smallest_eigval        (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "    energy_error           (chain, draw) float64 0.0 0.3789 ... 1.379 -1.532\n",
       "    ...                     ...\n",
       "    tree_depth             (chain, draw) int64 1 2 1 2 2 2 2 2 ... 1 2 2 2 2 2 1\n",
       "    energy                 (chain, draw) float64 2.506e+03 ... 2.509e+03\n",
       "    max_energy_error       (chain, draw) float64 0.817 0.7752 ... 1.835 -1.532\n",
       "    reached_max_treedepth  (chain, draw) bool False False False ... False False\n",
       "    process_time_diff      (chain, draw) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
       "    acceptance_rate        (chain, draw) float64 0.4418 0.6464 ... 0.2306 1.0\n",
       "Attributes:\n",
       "    created_at:                  2024-12-26T03:18:17.455046\n",
       "    arviz_version:               0.17.1\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.16.2\n",
       "    sampling_time:               19.907405138015747\n",
       "    tuning_steps:                1000\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.13.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7e1a2b4f-58c6-4e12-a7c4-af83ede2161f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7e1a2b4f-58c6-4e12-a7c4-af83ede2161f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-444da715-9d11-47ed-8482-d928f167c57b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-444da715-9d11-47ed-8482-d928f167c57b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-b603a038-eb36-4e57-875d-f99f0a4b55e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b603a038-eb36-4e57-875d-f99f0a4b55e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8baf7c57-94cb-43a3-b8d6-dc3caf3d84d5' class='xr-var-data-in' type='checkbox'><label for='data-8baf7c57-94cb-43a3-b8d6-dc3caf3d84d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-07452d4c-1bac-48af-8114-8677ae2df03e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07452d4c-1bac-48af-8114-8677ae2df03e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3bbe3a3-12e0-40c5-b7fa-896a99828308' class='xr-var-data-in' type='checkbox'><label for='data-a3bbe3a3-12e0-40c5-b7fa-896a99828308' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2444a16-b41f-41f9-a756-adb0a6d08876' class='xr-section-summary-in' type='checkbox'  ><label for='section-f2444a16-b41f-41f9-a756-adb0a6d08876' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.687e+04 9.687e+04 ... 9.688e+04</div><input id='attrs-294652cc-e27f-468b-bd67-880f9992a3a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-294652cc-e27f-468b-bd67-880f9992a3a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcee60e0-d72f-474e-8e32-c8a375df1218' class='xr-var-data-in' type='checkbox'><label for='data-dcee60e0-d72f-474e-8e32-c8a375df1218' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[96874.5507587, 96874.5512139, 96874.5520338, ..., 96875.0942942,\n",
       "        96875.0949499, 96875.0954215],\n",
       "       [96874.5639484, 96874.5642554, 96874.5652475, ..., 96876.0900614,\n",
       "        96876.0908972, 96876.0915164],\n",
       "       [96876.0517482, 96876.0528926, 96876.0534566, ..., 96877.735456 ,\n",
       "        96877.7359583, 96877.7364566],\n",
       "       [96878.7010462, 96878.7014641, 96878.7019614, ..., 96879.1889814,\n",
       "        96879.1894122, 96879.1898785]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8867 0.8867 ... 0.9589 0.9589</div><input id='attrs-b8519209-99f7-4d41-b3ec-ebbbe965edc4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8519209-99f7-4d41-b3ec-ebbbe965edc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79381ff6-d42d-49d9-846b-3fab9d1fa05f' class='xr-var-data-in' type='checkbox'><label for='data-79381ff6-d42d-49d9-846b-3fab9d1fa05f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.88673829, 0.88673829, 0.88673829, ..., 0.88673829, 0.88673829,\n",
       "        0.88673829],\n",
       "       [1.19118935, 1.19118935, 1.19118935, ..., 1.19118935, 1.19118935,\n",
       "        1.19118935],\n",
       "       [1.03472703, 1.03472703, 1.03472703, ..., 1.03472703, 1.03472703,\n",
       "        1.03472703],\n",
       "       [0.95890186, 0.95890186, 0.95890186, ..., 0.95890186, 0.95890186,\n",
       "        0.95890186]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-2efba113-2eed-4c68-a7b3-84de8f14040c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2efba113-2eed-4c68-a7b3-84de8f14040c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23d7fc25-350d-4092-96a5-268825a08c67' class='xr-var-data-in' type='checkbox'><label for='data-23d7fc25-350d-4092-96a5-268825a08c67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.505e+03 ... -2.505e+03</div><input id='attrs-c9f7eca6-ebf6-4a58-bcb1-8a46b10fb757' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9f7eca6-ebf6-4a58-bcb1-8a46b10fb757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-999f1902-0529-4b68-b378-db12f571645e' class='xr-var-data-in' type='checkbox'><label for='data-999f1902-0529-4b68-b378-db12f571645e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-2504.61894693, -2505.97520995, -2504.34548924, ...,\n",
       "        -2504.62610013, -2504.91856054, -2506.40701621],\n",
       "       [-2509.74171434, -2506.07372305, -2503.94094039, ...,\n",
       "        -2504.39911793, -2504.54750845, -2504.17618424],\n",
       "       [-2505.59581499, -2506.16227297, -2505.77602263, ...,\n",
       "        -2505.69512432, -2504.1012377 , -2505.3833691 ],\n",
       "       [-2506.26785555, -2505.25507718, -2504.35655634, ...,\n",
       "        -2505.23299752, -2510.04799814, -2505.48300948]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1752fdec-cc3a-406b-a161-a20e32ddfb01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1752fdec-cc3a-406b-a161-a20e32ddfb01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-953c1c64-9e27-4205-84a4-ed5c6fc6838e' class='xr-var-data-in' type='checkbox'><label for='data-953c1c64-9e27-4205-84a4-ed5c6fc6838e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.3789 -0.4692 ... 1.379 -1.532</div><input id='attrs-813e6ca6-7a54-49f3-9ed1-37d9e9b664d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-813e6ca6-7a54-49f3-9ed1-37d9e9b664d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-391439ad-1763-47a2-97d9-9107ebf35c32' class='xr-var-data-in' type='checkbox'><label for='data-391439ad-1763-47a2-97d9-9107ebf35c32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.        ,  0.37888897, -0.46922452, ...,  0.07779758,\n",
       "         0.10003432,  0.42360689],\n",
       "       [ 0.95616873, -1.25263181, -0.61303598, ..., -0.04139742,\n",
       "         0.03333186, -0.08533536],\n",
       "       [-0.27793972,  0.17253408,  0.0033868 , ...,  0.48285258,\n",
       "        -0.48607594,  0.36360805],\n",
       "       [ 0.34633579, -0.30290568, -0.28783831, ..., -0.55473968,\n",
       "         1.37889369, -1.53188074]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.072 1.072 1.072 ... 1.048 1.048</div><input id='attrs-3e3eb541-78aa-42a3-93ff-4af2d4367296' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e3eb541-78aa-42a3-93ff-4af2d4367296' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f713e8e2-5ad9-4d29-ac63-b5102d20e61a' class='xr-var-data-in' type='checkbox'><label for='data-f713e8e2-5ad9-4d29-ac63-b5102d20e61a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.07164616, 1.07164616, 1.07164616, ..., 1.07164616, 1.07164616,\n",
       "        1.07164616],\n",
       "       [1.10963024, 1.10963024, 1.10963024, ..., 1.10963024, 1.10963024,\n",
       "        1.10963024],\n",
       "       [1.11382582, 1.11382582, 1.11382582, ..., 1.11382582, 1.11382582,\n",
       "        1.11382582],\n",
       "       [1.04827499, 1.04827499, 1.04827499, ..., 1.04827499, 1.04827499,\n",
       "        1.04827499]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0002475 0.0006844 ... 0.00021</div><input id='attrs-7067836e-6a6d-41a4-81b9-9f5b52d3225d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7067836e-6a6d-41a4-81b9-9f5b52d3225d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fd0f7dc-ea2f-4f4a-8574-a8d4784c9016' class='xr-var-data-in' type='checkbox'><label for='data-2fd0f7dc-ea2f-4f4a-8574-a8d4784c9016' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0002475, 0.0006844, 0.0002675, ..., 0.0004782, 0.0003944,\n",
       "        0.0004016],\n",
       "       [0.000222 , 0.0007541, 0.0005178, ..., 0.0007091, 0.0005126,\n",
       "        0.0005086],\n",
       "       [0.0010186, 0.0004644, 0.0004761, ..., 0.0004138, 0.0004143,\n",
       "        0.0003941],\n",
       "       [0.0003448, 0.0003421, 0.0006068, ..., 0.0003336, 0.0003901,\n",
       "        0.00021  ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-041e069e-b4f7-43d2-8d39-d23444bda25b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-041e069e-b4f7-43d2-8d39-d23444bda25b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03c4b140-08d8-46a9-ac84-44e74d6afb52' class='xr-var-data-in' type='checkbox'><label for='data-03c4b140-08d8-46a9-ac84-44e74d6afb52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.0 1.0 3.0 ... 3.0 3.0 3.0 1.0</div><input id='attrs-45b524bf-5e4a-41d4-ad77-2bc84c627bf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45b524bf-5e4a-41d4-ad77-2bc84c627bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da3de16e-f0b3-436f-9411-2a9e2ff7e15f' class='xr-var-data-in' type='checkbox'><label for='data-da3de16e-f0b3-436f-9411-2a9e2ff7e15f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1., 3., 1., ..., 3., 3., 3.],\n",
       "       [1., 3., 3., ..., 3., 3., 3.],\n",
       "       [3., 3., 3., ..., 3., 3., 3.],\n",
       "       [3., 1., 3., ..., 3., 3., 1.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 -2 1 -3 3 -3 3 ... 1 -2 1 0 2 2 1</div><input id='attrs-ba63d72b-a763-425a-aab0-cda6590cfdac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba63d72b-a763-425a-aab0-cda6590cfdac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05b15ee3-02be-47d8-bbb0-c1ed62a2e162' class='xr-var-data-in' type='checkbox'><label for='data-05b15ee3-02be-47d8-bbb0-c1ed62a2e162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0, -2,  1, ..., -3, -2, -2],\n",
       "       [ 1, -2,  2, ...,  2, -3, -3],\n",
       "       [ 3, -3, -2, ..., -2, -2, -2],\n",
       "       [-1, -1, -3, ...,  2,  2,  1]], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 1 2 2 2 2 2 ... 2 1 2 2 2 2 2 1</div><input id='attrs-9f09898c-c3b2-429f-b0f0-c63d864125f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f09898c-c3b2-429f-b0f0-c63d864125f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4d33bbb-a182-4a21-a49d-2ba73194a477' class='xr-var-data-in' type='checkbox'><label for='data-f4d33bbb-a182-4a21-a49d-2ba73194a477' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 2, 1, ..., 2, 2, 2],\n",
       "       [1, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 2, 2, ..., 2, 2, 2],\n",
       "       [2, 1, 2, ..., 2, 2, 1]], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.506e+03 2.507e+03 ... 2.509e+03</div><input id='attrs-dbe40bcb-65e5-4ec5-84f4-7546cd5e4292' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbe40bcb-65e5-4ec5-84f4-7546cd5e4292' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8caea908-9d11-45d5-89cf-98c0f7aae6b5' class='xr-var-data-in' type='checkbox'><label for='data-8caea908-9d11-45d5-89cf-98c0f7aae6b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2506.25996771, 2507.44360113, 2505.51080849, ..., 2505.47797531,\n",
       "        2505.19101517, 2508.79293337],\n",
       "       [2510.18715088, 2509.32515475, 2508.22812741, ..., 2505.18878758,\n",
       "        2504.99794028, 2506.12932346],\n",
       "       [2506.51422852, 2506.69272688, 2508.2631311 , ..., 2507.90443194,\n",
       "        2506.19873447, 2506.29070039],\n",
       "       [2508.39864415, 2506.26860699, 2507.97938313, ..., 2508.11562584,\n",
       "        2512.3185041 , 2508.6978875 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.817 0.7752 ... 1.835 -1.532</div><input id='attrs-e0dfe022-fe8b-4890-8c08-f3491dbd4813' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0dfe022-fe8b-4890-8c08-f3491dbd4813' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67cba1c3-f847-4a0e-a2fc-62dd04c96a23' class='xr-var-data-in' type='checkbox'><label for='data-67cba1c3-f847-4a0e-a2fc-62dd04c96a23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.81697274,  0.77515246, -0.46922452, ...,  0.34826293,\n",
       "        -0.10636683,  1.05678127],\n",
       "       [ 0.95616873, -1.4554786 ,  1.33857692, ...,  0.20932822,\n",
       "         0.16374315,  0.63297168],\n",
       "       [-0.42194365,  0.17253408,  0.46517932, ...,  1.59343835,\n",
       "        -0.48607594,  0.6178961 ],\n",
       "       [ 1.10653206, -0.30290568,  1.06823777, ..., -0.55473968,\n",
       "         1.8350542 , -1.53188074]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-877714b8-707b-4c03-9387-24c622bad7c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-877714b8-707b-4c03-9387-24c622bad7c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc1f2a3b-8b02-45e2-803b-1815e1c44e09' class='xr-var-data-in' type='checkbox'><label for='data-dc1f2a3b-8b02-45e2-803b-1815e1c44e09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5caf757e-9b6c-4668-b530-5506d6f9d9be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5caf757e-9b6c-4668-b530-5506d6f9d9be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-363a0be5-d25d-40b8-8034-018764d5d60e' class='xr-var-data-in' type='checkbox'><label for='data-363a0be5-d25d-40b8-8034-018764d5d60e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4418 0.6464 1.0 ... 0.2306 1.0</div><input id='attrs-741928a4-5d8b-4f9c-a237-405340ff62c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-741928a4-5d8b-4f9c-a237-405340ff62c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d48718be-9d03-4b9f-8ee7-fb2220d1b3fb' class='xr-var-data-in' type='checkbox'><label for='data-d48718be-9d03-4b9f-8ee7-fb2220d1b3fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.44176698, 0.64644231, 1.        , ..., 0.85234775, 0.96826879,\n",
       "        0.61835948],\n",
       "       [0.38436267, 1.        , 0.71641943, ..., 0.89537595, 0.93872586,\n",
       "        0.77878783],\n",
       "       [1.        , 0.94717654, 0.81483031, ..., 0.43072441, 0.91964539,\n",
       "        0.70611436],\n",
       "       [0.47411178, 1.        , 0.67441725, ..., 0.93155561, 0.23061767,\n",
       "        1.        ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7833a9c7-0f6a-45e2-b7ce-b4bea1e9b5ce' class='xr-section-summary-in' type='checkbox'  ><label for='section-7833a9c7-0f6a-45e2-b7ce-b4bea1e9b5ce' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76be1f43-b843-477f-8d6d-ee93943b30d0' class='xr-index-data-in' type='checkbox'/><label for='index-76be1f43-b843-477f-8d6d-ee93943b30d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da6dbe2a-6b65-4ea1-98d9-66215cddaba6' class='xr-index-data-in' type='checkbox'/><label for='index-da6dbe2a-6b65-4ea1-98d9-66215cddaba6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2ba98af-2726-412f-97fc-fb506f7d019f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2ba98af-2726-412f-97fc-fb506f7d019f' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-26T03:18:17.455046</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>19.907405138015747</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data609894ee-628f-4435-9004-9acf6da14d32\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data609894ee-628f-4435-9004-9acf6da14d32\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (log_RTs_obs: 2326)\n",
       "Coordinates:\n",
       "  * log_RTs_obs  (log_RTs_obs) int32 0 1 2 3 4 5 ... 2321 2322 2323 2324 2325\n",
       "Data variables:\n",
       "    log_RTs      (log_RTs_obs) float64 7.29 7.394 7.788 ... 8.1 7.757 7.507\n",
       "Attributes:\n",
       "    created_at:                  2024-12-26T03:18:17.461051\n",
       "    arviz_version:               0.17.1\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.16.2\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.13.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-91e0a6f1-bbff-45e4-9372-6804fb84e5a1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91e0a6f1-bbff-45e4-9372-6804fb84e5a1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>log_RTs_obs</span>: 2326</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-808cc141-0674-4568-b49d-80c4a46f9fef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-808cc141-0674-4568-b49d-80c4a46f9fef' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_RTs_obs</span></div><div class='xr-var-dims'>(log_RTs_obs)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2322 2323 2324 2325</div><input id='attrs-54b4ac39-b950-4771-9110-ef663e75383a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54b4ac39-b950-4771-9110-ef663e75383a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dab52370-9996-4729-a433-7dfef62d212e' class='xr-var-data-in' type='checkbox'><label for='data-dab52370-9996-4729-a433-7dfef62d212e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2323, 2324, 2325])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8eae9767-1842-40f6-973b-e9ca9e101c7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8eae9767-1842-40f6-973b-e9ca9e101c7a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_RTs</span></div><div class='xr-var-dims'>(log_RTs_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.29 7.394 7.788 ... 7.757 7.507</div><input id='attrs-92a7e394-8e74-4a3e-9ae7-45d8a479a16d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92a7e394-8e74-4a3e-9ae7-45d8a479a16d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-876f904a-45fa-4867-97c6-5695fe00a7e8' class='xr-var-data-in' type='checkbox'><label for='data-876f904a-45fa-4867-97c6-5695fe00a7e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([7.28961052, 7.39387829, 7.78779688, ..., 8.09955428, 7.75705114,\n",
       "       7.50714108])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a14c3e51-b420-4f62-bb5c-bb8154321ae8' class='xr-section-summary-in' type='checkbox'  ><label for='section-a14c3e51-b420-4f62-bb5c-bb8154321ae8' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>log_RTs_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2866a6b5-35fd-405d-b351-988f038ecb96' class='xr-index-data-in' type='checkbox'/><label for='index-2866a6b5-35fd-405d-b351-988f038ecb96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;log_RTs_obs&#x27;, length=2326))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6544f853-79f0-41d5-8382-6c9620ee341f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6544f853-79f0-41d5-8382-6c9620ee341f' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-12-26T03:18:17.461051</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0</dd></dl></div></li></ul></div></div><br></div>\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_pooled_trace"
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   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义计算 MAE 函数\n",
    "from statistics import median\n",
    "def MAE(trace):\n",
    "    # 计算每个X取值下对应的后验预测模型的均值\n",
    "    pre_x = trace.posterior_predictive[\"log_RTs\"].stack(sample=(\"chain\", \"draw\"))\n",
    "    pre_y_mean = pre_x.mean(axis=1).values\n",
    "\n",
    "    # 提取观测值Y，提取对应Y值下的后验预测模型的均值\n",
    "    MAE = pd.DataFrame({\n",
    "        \"ppc_mean\": pre_y_mean,\n",
    "        \"original\": trace.observed_data.log_RTs.values\n",
    "    })\n",
    "\n",
    "    # 计算预测误差\n",
    "    MAE[\"pre_error\"] = abs(MAE[\"original\"] -\\\n",
    "                            MAE[\"ppc_mean\"])\n",
    "\n",
    "    # 最后，计算预测误差的中位数\n",
    "    MAE = median(MAE.pre_error)\n",
    "    return MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义\n",
    "def counter_outlier(model_trace, hdi_prob=0.95):\n",
    "    # 将az.summary生成的结果存到hdi_multi这个变量中，该变量为数据框\n",
    "    hdi = az.summary(model_trace.posterior_predictive, kind=\"stats\", hdi_prob=hdi_prob)\n",
    "    lower = hdi.iloc[:,2].values\n",
    "    upper = hdi.iloc[:,3].values\n",
    "\n",
    "    # 将原数据中的自我控制分数合并，便于后续进行判断\n",
    "    y_obs = model_trace.observed_data[\"log_RTs\"].values\n",
    "\n",
    "    # 判断原数据中的压力分数是否在后验预测的95%可信区间内，并计数\n",
    "    hdi[\"verify\"] = (y_obs <= lower) | (y_obs >= upper)\n",
    "    hdi[\"y_obs\"] = y_obs\n",
    "    hdi_num = sum(hdi[\"verify\"])\n",
    "\n",
    "    return hdi_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model       MAE  Outliers\n",
       "0     完全池化  0.517794       125\n",
       "1     变化截距  0.316176       158\n",
       "2  变化截距、斜率  0.307307       154"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将每个模型的PPC储存为列表\n",
    "ppc_samples_list = [complete_pooled_trace, var_inter_trace, var_both_trace]\n",
    "model_names = [\"完全池化\", \"变化截距\", \"变化截距、斜率\"]\n",
    "\n",
    "# 建立一个空列表来存储结果\n",
    "results_list = []\n",
    "\n",
    "# 遍历模型并计算MAE和超出95%hdi的值\n",
    "for model_name, ppc_samples in zip(model_names, ppc_samples_list):\n",
    "    outliers = counter_outlier(ppc_samples)\n",
    "    MAEs = MAE(ppc_samples)\n",
    "    results_list.append({'Model': model_name, 'MAE':MAEs, 'Outliers': outliers})\n",
    "\n",
    "# 从结果列表创建一个DataFrame\n",
    "results_df = pd.DataFrame(results_list)\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>elpd_loo</th>\n",
       "      <th>p_loo</th>\n",
       "      <th>elpd_diff</th>\n",
       "      <th>weight</th>\n",
       "      <th>se</th>\n",
       "      <th>dse</th>\n",
       "      <th>warning</th>\n",
       "      <th>scale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>model3(hierarchy both)</th>\n",
       "      <td>0</td>\n",
       "      <td>-1941.431752</td>\n",
       "      <td>13.064896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.884853</td>\n",
       "      <td>37.106601</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model2(hierarchical intercept)</th>\n",
       "      <td>1</td>\n",
       "      <td>-1950.120395</td>\n",
       "      <td>8.071729</td>\n",
       "      <td>8.688643</td>\n",
       "      <td>0.115147</td>\n",
       "      <td>36.892828</td>\n",
       "      <td>4.741897</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model1(complete pooling)</th>\n",
       "      <td>2</td>\n",
       "      <td>-2502.072721</td>\n",
       "      <td>3.013059</td>\n",
       "      <td>560.640969</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.205582</td>\n",
       "      <td>27.353555</td>\n",
       "      <td>False</td>\n",
       "      <td>log</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                rank     elpd_loo      p_loo   elpd_diff  \\\n",
       "model3(hierarchy both)             0 -1941.431752  13.064896    0.000000   \n",
       "model2(hierarchical intercept)     1 -1950.120395   8.071729    8.688643   \n",
       "model1(complete pooling)           2 -2502.072721   3.013059  560.640969   \n",
       "\n",
       "                                  weight         se        dse  warning scale  \n",
       "model3(hierarchy both)          0.884853  37.106601   0.000000    False   log  \n",
       "model2(hierarchical intercept)  0.115147  36.892828   4.741897    False   log  \n",
       "model1(complete pooling)        0.000000  34.205582  27.353555    False   log  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_list = {\n",
    "    \"model1(complete pooling)\":complete_pooled_trace,\n",
    "    \"model2(hierarchical intercept)\":var_inter_trace,\n",
    "    \"model3(hierarchy both)\":var_both_trace,\n",
    "}\n",
    "az.compare(comparison_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过 `arviz.compare` 方法来对比多个模型的 elpd。从下面结果可见： \n",
    " \n",
    "- 模型3的 elpd_loo 最大，表明它对**样本外数据**的预测性能最好。  \n",
    "- 而模型1的 elpd_loo 最小，表明它的预测性能最差。  \n",
    "\n",
    "因此，根据模型评估的结果，可以发现模型3（变化截距和变化斜率）的结果在3个模型中是最好的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 贝叶斯统计推断\n",
    "\n",
    "模型比较发现，模型3（变化截距和变化斜率）的结果在3个模型中是最好的。最后，将使用 HDI + ROPE 和贝叶斯因子（Bayes Factor，BF）来进行统计推断。\n",
    "\n",
    "#### HDI + ROPE 的统计推断\n",
    "\n",
    "* 反应时差异的后验分布平均值为 -281 ms，然而，这一数值并不足以断定实验条件对反应时间有显著的减少作用。\n",
    "* 95% HDI 范围为 [-608 ms, 48 ms]，表明后验分布中95%的概率下的反应时差异位于此区间。由于95% HDI 包含了0，并且分布主要集中在负值方向，但这一趋势并不足以证明存在显著的效应。\n",
    "* ROPE 设定了一个 [-30 ms, 30 ms] 的实用等效区间，用以判断反应时差异是否具有实际意义。ROPE 内的概率仅为1.6%，尽管这表明在大多数情况下反应时差异超出了可忽略的范围，但这一差异仍不足以被视为显著。\n",
    "\n",
    "\n",
    "#### 贝叶斯因子（Bayes Factor，BF）\n",
    "\n",
    "反应时的差异（beta_1）在统计上和实际意义上均不显著。\n",
    "* 数据强烈支持 无效假设（beta_1 = 0），即反应时差异可能不存在或非常微弱。\n",
    "* 贝叶斯因子 BF_10 = 0.01 提供了明确的证据，表明 beta_1 不显著。\n",
    "* 从后验分布来看，数据更新后 beta_1 的可能值仍然集中在 0 附近，进一步支持无效假设。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从贝叶斯模型的后验分布中提取参数\n",
    "def inv_log(mu, sigma):\n",
    "    return np.exp(mu + (sigma ** 2) / 2)\n",
    "\n",
    "Intercept_mu = var_both_trace.posterior.stack(sample=(\"chain\", \"draw\")).get(\"Intercept\")\n",
    "Coherence_mu = var_both_trace.posterior.stack(sample=(\"chain\", \"draw\")).get(\"Coherence\")\n",
    "Intercept_sigma = var_both_trace.posterior.stack(sample=(\"chain\", \"draw\")).get(\"1|subj_id_sigma\")\n",
    "Coherence_sigma = var_both_trace.posterior.stack(sample=(\"chain\", \"draw\")).get(\"Coherence|subj_id_sigma\")\n",
    "\n",
    "# 计算两个条件下的反应时间\n",
    "rt_coh_5 = inv_log(Intercept_mu, Intercept_sigma)\n",
    "rt_coh_10 = inv_log(Intercept_mu+Coherence_mu, Coherence_sigma)\n",
    "rt_coh_5 = rt_coh_5[(rt_coh_5 >= 300) & (rt_coh_5 <= 1500)]\n",
    "rt_coh_10 = rt_coh_10[(rt_coh_10 >= 300) & (rt_coh_10 <= 1500)]\n",
    "\n",
    "rt_diff = rt_coh_10 - rt_coh_5\n",
    "rt_diff = rt_diff.values\n",
    "\n",
    "# 定义 ROPE 区间，根据研究的需要指定实际等效范围\n",
    "rope_interval = [-30, 30]\n",
    "\n",
    "# 绘制后验分布，显示 HDI 和 ROPE\n",
    "az.plot_posterior(\n",
    "    {\"RT Difference\":rt_diff},\n",
    "    hdi_prob=0.95,\n",
    "    rope=rope_interval,\n",
    "    figsize=(8, 5),\n",
    "    textsize=12\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [1|subj_id_offset, 1|subj_id_sigma, Coherence, Coherence|subj_id_offset, Coherence|subj_id_sigma, Intercept, log_RTs, log_RTs_sigma]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行贝叶斯因子计算，需要采样先验分布\n",
    "var_both_trace.extend(var_both_model.prior_predictive(random_seed=84735) )\n",
    "\n",
    "# 绘制贝叶斯因子图\n",
    "az.plot_bf(var_both_trace, var_name=\"Coherence\", ref_val=0)\n",
    "\n",
    "# 设置 x 轴的范围\n",
    "plt.xlim(-0.5, 0.5) \n",
    "\n",
    "# 去除上框线和右框线\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "\n",
    "通过模型建立和模型比较，相比于模型1和模型2，模型3（变化截距和变化斜率）的效果最好。因此，在随机点运动范式中，反应时间显著受到随机点运动方向一致性比例的影响。\n",
    "\n",
    "具体而言，模型3考虑了变化截距和变化斜率，这意味着它不仅捕捉到了不同条件下反应时间的平均差异，还考虑了这些差异随条件变化的趋势。相比之下，模型1和模型2未能充分捕捉到这些复杂的变化模式，因而在预测精度和模型拟合度上表现不如模型3。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 大作业注意事项\n",
    "\n",
    "在完成大作业时，请注意以下几点要求：\n",
    "\n",
    "1. **文档**：\n",
    "   - 请按照APA7论文格式撰写文档，确保内容完整、格式规范。\n",
    "   - 文档应包括研究背景、方法、结果和讨论等部分，详细描述研究过程和发现。\n",
    "\n",
    "2. **和鲸Notebook演示（或PPT）**：\n",
    "   - 使用和鲸Notebook或PPT进行演示，清晰展示研究的各个步骤和结果。\n",
    "   - 演示内容应包括数据处理、模型构建、结果分析和结论等部分。\n",
    "\n",
    "3. **代码**：\n",
    "   - 提交完整的代码，确保代码可以运行并生成预期结果。\n",
    "   - 代码应包括数据导入、预处理、模型构建、拟合和结果分析等部分。\n",
    "   - 请在代码中添加必要的注释。\n",
    "\n",
    "\n",
    "💡互评环节：\n",
    "\n",
    "在2025年1月3号的最后一次课时，各小组将进行大作业汇报。每组可以对其他汇报组进行评分，评分标准包括文档规范性、演示效果和代码运行情况等。\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pymc5_3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
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 },
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